where \(\lambda \)  is a regularization parameter, \(N\)  is the number of input samples, also called the batch size, and \(w\)  is a vector containing all the weights of the network. We use the identity linear function for the output node and tanh activations for the rest. Finally, we use the ADAM optimizer [1]}, a more advanced version of the gradient descent algorithm, as our learning algorithm for minimizing the cost function.
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